Different Types of Business Analytics – Definition, Applications & More

In the pulsating realm of business analytics, a symphony of innovation unfolds, showcasing a trinity of methodologies that sculpt the present and forecast the future. These three pillars, Descriptive, Predictive, and Prescriptive Analytics, serve as the navigational stars guiding modern enterprises through the cosmic sea of data. The BLS prophecy heralds an 11% surge in business analytics employment, a meteoric rise transcending temporal bounds from 2021 to 2031.

This propulsion, swifter than the industry’s average, illuminates the escalating trend of flexibility in labour, an essence permeating the very fabric of 2023’s professional landscape. In this era where data isn’t just king but the entire kingdom, the applications of business analytics unfurl a tapestry of possibilities, painting a portrait of informed decisions, streamlined operations, and unprecedented growth.

What is Business Analytics? 

Business analytics encompasses a range of methodologies and technological tools utilised to address business challenges by employing data analysis, statistical models, and quantitative approaches. This iterative and systematic process involves thoroughly delving into an organisation’s data, placing a strong focus on statistical analysis to steer decision-making.

Data-driven companies view their data as a valuable asset and are continually seeking ways to exploit that to gain a strategic advantage. Success with business analytics depends on having good data, analysts who are proficient with both the technology and in the line of business, and a strong focus on using the data to gain meaningful insight to drive business decisions.

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The major kinds of business analytics include descriptive, diagnostic, predictive, and prescriptive analytics. Recently, cognitive analytics has joined the fray. It uses AI, ML, and deep learning. Each of these kinds of business analytics is separately powerful but all together much more powerful.

1. Descriptive Analytics

The set of descriptive analytics is generally regarded as the fundamental tool in analysing historical data for realising how a unit responds across a predefined set of variables. The main focus of Descriptive Analytics is the assessment of KPIs so that there will be proper realisation on the part of a business about its present condition:

This kind of analysis approach is done via a structured process which involves five essential steps:

• Definition of Relevant Business Metrics: It takes the first step in identifying and choosing the most relevant metrics related to the objectives and goals of an organisation.

• Data Identification: To understand the business in its current state, it requires identification of what data sources need to be supplied for the analysis.

• Data Collection and Preparation: After identification, the data go through a series of processes such as de-duplication, transformation, and cleansing for accuracy and consistency.

• Pattern Analysis: In this stage, the underlying patterns and trends are observed in the data, which will help in measuring performance against predetermined metrics.

• Visualisation and Reporting: For the understanding of non-analytics users, the insight gained during analysis is converted into a visual format like charts and graphs.

Examples of Descriptive Analytics applications cover a vast area:

• Summarising Past Events: Past Event Summarization: Converting historical information to comprehend patterns and events occurring for an informed decision on past instances.
• Data Exchange and Social Media Usage Analysis: Analytical trends in data exchange and social media interactions for facilitating customer behaviour and market trend analysis.

Descriptive analytics thus gives a big-picture view of the current status of a business; therefore, it becomes vital in strategy development based on historical data to decode patterns and trends. Descriptive analytics is thus able to convert complex information into easy-to-understand formats that make businesses take proactive steps toward growth and improvements.

2. Diagnostic Analytics

Diagnostic analytics is a main category of business analytics that aims to explain the ‘why’ of events in the past. It is, therefore, complementary to Descriptive Analytics, which just describes ‘what’ and ‘how’ something happened in the past. Diagnostic Analytics will find out what causes and drivers are behind it by employing techniques like drill-downs, data mining, discovery, and correlations.

It is an advanced analytical technique and acts as a precursor to Descriptive Analytics, thus forming a foundation to understand the underlying logic responsible for certain results which turn up in various areas of business and fields such as finance, marketing, cybersecurity, and many others.

Key aspects that make up Diagnostic Analytics include:

• Drill-Downs: Zooming down to reach detailed subsets of data for uncovering particular insights and trends leading to any specific outcome.
• Data Mining: Patterns and relationships within a large dataset are extracted to unearth hidden correlations and casual interdependencies.
• Data Discovery: This is the search for data in forms such as visualisations and interactivity to uncover the unexpected within a trend or anomaly.
• Correlations: It shows the relationships among different variables and what would happen when one of the key factors changes.

There are several areas where the use of diagnostic analytics is used to determine the reasons behind something happening or not. Some examples are given below:

• Examining Market Demand: The reason for market demand fluctuation for certain products or services can be determined through it.
• Detection of Technical Issues: It is used to find out why technical malfunctioning or inefficiency in any system or process is occurring.
• Explanation of Customer Behaviour: Understanding the motives of customers for certain actions and preferences, to create more rounded experiences.
• Creation of Better Organisational Culture: Internal data analysis to understand what people are happy with and what depresses them, in order to work productively.

Diagnostic analytics is a crucial tool in unveiling these deeper layers of data, thus getting insights that will allow businesses not only to understand what took place in the past but proactively to root out problems and optimise strategies for future success.

3. Predictive Analytics

Predictive analytics is on the frontline to project what could actually happen in the future using historical data. This forward-looking analytic technique employs several advanced techniques-data mining, machine learning algorithms, and statistical modelling-to predict the likelihood of certain events.

The key motive behind Predictive Analytics is to render insight that can permit proactive decisions and strategic planning across diverse facets of the operations. Applications range from the following:

• Improved Customer Service: With predictive analytics, it is possible to determine customer preferences and behaviours by which services can be tailored to meet the individual needs with which a better relationship and loyalty can be created.
• Operational Efficiency: The future trends and demands can only be understood if the optimization of the business operations is really effective to ensure that resources are put in place properly to cater to future needs.
• Fraud Detection and Risk Management: The predictive models flag any probable fraudulent activities or assess risks, hence helping intervene and mitigate it well in time.
• Profitable Customer Growth: With an understanding of customer responses and behaviours, businesses can focus efforts on nurturing relationships with profitable customer segments.

Various applications of Predictive Analytics are effective in their outcomes, including:

• Predicting Customer Preferences: Predictive analytics can be used to identify which product or service a customer may be interested in by borrowing data from past behaviour and preference.
• Employee Intentions Detection: Factors indicating the likelihood of employee turnover or commitment to the organisation can be identified through predictive analytics.
• Product Recommendations: Suggesting products or services to customers by keeping in view the history of purchases and preferences of every customer.
• Predictive Staff and Resource Need: Forecast staffing level and resource utilisation based on forecasted demand.

In general, Predictive Analytics enables any organisation to be free from being at the mercy of reactive approaches by taking the best from history to predict what could happen. From that viewpoint, companies are capable of deciding, optimising, leveraging opportunities, and positioning themselves better for the future.

4. Prescriptive Analytics

Prescriptive Analytics is an innovative way of looking at data analysis, and it provides practical recommendations on possible future scenarios with support from previous performance. The advanced analytical technique makes use of a suite of tools, statistics, and machine learning algorithms, pulling from internal and external sources of data to make informed suggestions.

Fundamentally, prescriptive analytics goes further than predictive insights into a detailed understanding of what may happen in the future, when it would happen, and why it happens.

Applications of Prescriptive Analytics can be found in virtually every industry and sector. Tracking fluctuating manufacturing price: Through its analysis, Prescriptive Analytics can track price fluctuations in the past so it will strategize on controlling future fluctuations more effectively.

• Smarter Equipment Management: It uses historical data on the performance of specific equipment and suggests when it would be best maintained or what procedures would lead to its optimal use.
• Prescriptive Action Suggestion: It provides personalised suggestions after in-depth data analysis that helps decision-makers choose the best course of action.
• Price Modelling: Predictive analyses of price movements with Prescriptive Analytics create superior pricing models for products or services.
• Readmission Rate Evaluation: In the field of healthcare, this analytics approach helps predict and avoid cases of readmission by studying past data of patients and recommending preventive measures.
• Testing Strategies Identification: Prescriptive analytics identify past testing methodologies and results to design strategies for effective and efficient testing for various purposes.

Prescriptive analytics is thus proactive in nature, assisting business entities to prepare for the future by availing the power of past data. It does not stop at predicting but rather goes ahead to guide the decision-maker by recommending the best courses of action based on data-driven insights.

Prescriptive Analytics can also enable organisations to pursue the optimization of strategies, mitigation of risks, and realisation of opportunities by lending insight into what could happen in the future, coupled with the why. This makes the art of decision-making more knowledgeable and effective in industries.

5. Cognitive Analytics

Cognitive Analytics, based on the amalgamation of Artificial Intelligence (AI)  and Data Analytics, is now an emerging boom that has set new trends for the latest frontier in business analytics. The ability of this avant-garde trend to avail AI-powered capabilities in finding insights and optimal solutions hidden in millions of data records has outlived traditional ways of analytics.

The idea behind Cognitive Analytics, in simple terms, is to navigate the vastness of knowledge bases using advanced techniques to obtain optimal answers for posed questions. It examines not only structured data but even plunges into the unstructured stream emanating from images, text documents, emails, and postings on social media.

Cognitive Analytics covers a wide variety of analytics that aim to analyse huge volumes of data, monitor customer behaviour, and trends emerging in the market. Adding AI-driven capabilities to it interprets the data and learns from it, thus having insights from complex information and unveiling earlier unknown correlations.

Examples of Cognitive Analytics applications:

• Tapping Unstructured Data Sources: Cognitive Analytics, by tapping into the power of AI algorithms, unlocks insights from unstructured data sources such as images, text documents, emails, and social media posts. This goes beyond traditional structured data analysis and provides an all-round understanding of diverse formats of data.

• Monitoring Customer Behaviour and Emerging Trends: Cognitive Analytics is good at monitoring consumer behaviour and emerging trends by deciphering datasets for patterns and emerging trends. This helps the business make decisions well in advance to stay ahead of any dynamic market landscape.

The integration of AI-driven cognitive capabilities in the analysis of data serves as a remarkable change that empowers enterprises to unlock truly valuable insights from an array of sources previously unexploited. Driven by Cognitive Analytics, organisations are now able to decide based on the insights from the data, predict trends, and drive actionable insights toward innovation and strategic growth.

Why is Business Analytics Important?

Business analytics remains the backbone of organisational success, as it plays a major role in informed decision-making and in raising performance. The data-driven insights can help businesses steer their operations in the direction of efficiency, innovation, and strategic growth. Some of the benefits of using business analytics include:

• Improved decision-making
• Cost savings
• Enhanced customer experience
• Increased efficiency
• Improved strategic planning

Business Analytics Applications

Business analytics is one of the major requirements for modern business enterprise. It brings about a revolution in various sectors. Starting from healthcare to finance, its application can be seen in most sectors, which deliver vital insight and strategic guidance in varied decision-making processes. Some of the leading sectors where this plays a dominant role includes;

1. Banking

Business analytics draws useful insight from credit and debit card data into consumer spending habits, financial status, behavioural trends, and lifestyle preferences. This knowledge helps in providing customer-specific services, assessing risks, and fraud detection.

2. Customer Relationship Management (CRM)

Through analytics, CRM systems study demographics, buying patterns, and socio-economic information to offer personalised services for customer bonding and loyalty.

3. Finance

In this unstable financial environment, business analytics helps in budgeting, financial planning, forecasting, and portfolio management to arrive at decisions with much-needed insights.

4. Human Resources

Analysis of data on top candidates will thereby enable the HR department in talent acquisition, retention policy, and forecasting the best fit between the candidates and the culture of the organisation.

5. Manufacturing

Business analysts evaluate data to enhance operational effectiveness by highlighting elements that affect operations, including equipment downtimes, inventory levels, and maintenance costs-all of which enhances the capability to control inventories and supply chains.

6. Marketing

Business analytics reviews metrics involved in marketing, consumer behaviour, and market trends to advertise products with efficiency, take full advantage of social media, and showcase product preferences that will maximise marketing efforts.

These applications often overlap, and that is where the interrelationship of business analytics really comes in. By consolidating data-driven insights, diverse departments work more in harmony with each other to achieve shared organisational goals. Business analysts prove to be crucial in highlighting opportunities for improvement and driving coordinated strategies across departments. Business analytics help an organisation make its way through complexities, innovate, and plan continued success in a changing business environment.

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The Bottom Line

In the ever-changing business landscape of today, there is a strong requirement to focus on the usage of business analytics.  Hero Vired uses business analytics not because it is an option but because it is a compulsion to be in the race. In today’s world, where data does the load, the ability to find insights from big data can make all the difference in how successful one can be.

With the Accelerator Program in Business Analytics and Data Science at Hero Vired,  you will not only move with the times but actually lead from the front in times of innovation and strategic decision-making. The integration of descriptive, diagnostic, predictive, prescriptive, and cognitive analytics further equips us to unlock unparalleled insight, thus helping us to move toward on-time identification of trends, process optimization, and making informed decisions using data as a basis.